#!/bin/bash
  
  # 1g is as 1f but moving to a factorized TDNN (TDNN-F) model, re-tuning it, and
  #  switching to unconstrained egs (the last of which gives around 0.1%
  #  improvement).  (Note: I don't believe the Tedlium TDNN models were,
  #  previously, very well-tuned).
  
  # local/chain/compare_wer_general.sh exp/chain_cleaned/tdnn1f_sp_bi exp/chain_cleaned/tdnn1g_sp
  # System                tdnn1f_sp_bi tdnn1g_sp
  # WER on dev(orig)            8.9       7.9
  # WER on dev(rescored)        8.1       7.3
  # WER on test(orig)           9.1       8.0
  # WER on test(rescored)       8.6       7.6
  # Final train prob        -0.1026   -0.0637
  # Final valid prob        -0.1031   -0.0750
  # Final train prob (xent)   -1.4370   -0.9792
  # Final valid prob (xent)   -1.4670   -0.9951
  # Num-params                 6994800   9431072
  
  # steps/info/chain_dir_info.pl exp/chain_cleaned/tdnn1g_sp
  # exp/chain_cleaned/tdnn1g_sp: num-iters=108 nj=3..12 num-params=9.4M dim=40+100->3600 combine=-0.060->-0.060 (over 2) xent:train/valid[71,107,final]=(-1.30,-0.985,-0.979/-1.29,-1.00,-0.995) logprob:train/valid[71,107,final]=(-0.098,-0.065,-0.064/-0.100,-0.075,-0.075)
  
  ## how you run this (note: this assumes that the run_tdnn.sh soft link points here;
  ## otherwise call it directly in its location).
  # by default, with cleanup:
  # local/chain/run_tdnn.sh
  
  # without cleanup:
  # local/chain/run_tdnn.sh  --train-set train --gmm tri3 --nnet3-affix "" &
  
  set -e -o pipefail
  
  # First the options that are passed through to run_ivector_common.sh
  # (some of which are also used in this script directly).
  stage=0
  nj=30
  decode_nj=30
  min_seg_len=1.55
  xent_regularize=0.1
  dropout_schedule='0,0@0.20,0.5@0.50,0'
  
  train_set=train_cleaned
  gmm=tri3_cleaned  # the gmm for the target data
  num_threads_ubm=32
  nnet3_affix=_cleaned  # cleanup affix for nnet3 and chain dirs, e.g. _cleaned
  
  # The rest are configs specific to this script.  Most of the parameters
  # are just hardcoded at this level, in the commands below.
  train_stage=-10
  tree_affix=  # affix for tree directory, e.g. "a" or "b", in case we change the configuration.
  tdnn_affix=1g  #affix for TDNN directory, e.g. "a" or "b", in case we change the configuration.
  common_egs_dir=  # you can set this to use previously dumped egs.
  remove_egs=true
  
  # End configuration section.
  echo "$0 $@"  # Print the command line for logging
  
  . ./cmd.sh
  . ./path.sh
  . ./utils/parse_options.sh
  
  
  if ! cuda-compiled; then
    cat <<EOF && exit 1
  This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
  If you want to use GPUs (and have them), go to src/, and configure and make on a machine
  where "nvcc" is installed.
  EOF
  fi
  
  local/nnet3/run_ivector_common.sh --stage $stage \
                                    --nj $nj \
                                    --min-seg-len $min_seg_len \
                                    --train-set $train_set \
                                    --gmm $gmm \
                                    --num-threads-ubm $num_threads_ubm \
                                    --nnet3-affix "$nnet3_affix"
  
  
  gmm_dir=exp/$gmm
  ali_dir=exp/${gmm}_ali_${train_set}_sp_comb
  tree_dir=exp/chain${nnet3_affix}/tree_bi${tree_affix}
  lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_comb_lats
  dir=exp/chain${nnet3_affix}/tdnn${tdnn_affix}_sp_bi
  train_data_dir=data/${train_set}_sp_hires_comb
  lores_train_data_dir=data/${train_set}_sp_comb
  train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires_comb
  
  
  for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
      $lores_train_data_dir/feats.scp $ali_dir/ali.1.gz $gmm_dir/final.mdl; do
    [ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
  done
  
  if [ $stage -le 14 ]; then
    echo "$0: creating lang directory with one state per phone."
    # Create a version of the lang/ directory that has one state per phone in the
    # topo file. [note, it really has two states.. the first one is only repeated
    # once, the second one has zero or more repeats.]
    if [ -d data/lang_chain ]; then
      if [ data/lang_chain/L.fst -nt data/lang/L.fst ]; then
        echo "$0: data/lang_chain already exists, not overwriting it; continuing"
      else
        echo "$0: data/lang_chain already exists and seems to be older than data/lang..."
        echo " ... not sure what to do.  Exiting."
        exit 1;
      fi
    else
      cp -r data/lang data/lang_chain
      silphonelist=$(cat data/lang_chain/phones/silence.csl) || exit 1;
      nonsilphonelist=$(cat data/lang_chain/phones/nonsilence.csl) || exit 1;
      # Use our special topology... note that later on may have to tune this
      # topology.
      steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >data/lang_chain/topo
    fi
  fi
  
  if [ $stage -le 15 ]; then
    # Get the alignments as lattices (gives the chain training more freedom).
    # use the same num-jobs as the alignments
    steps/align_fmllr_lats.sh --nj 100 --cmd "$train_cmd" ${lores_train_data_dir} \
      data/lang $gmm_dir $lat_dir
    rm $lat_dir/fsts.*.gz # save space
  fi
  
  if [ $stage -le 16 ]; then
    # Build a tree using our new topology.  We know we have alignments for the
    # speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
    # those.
    if [ -f $tree_dir/final.mdl ]; then
      echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
      exit 1;
    fi
    steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
        --context-opts "--context-width=2 --central-position=1" \
        --cmd "$train_cmd" 4000 ${lores_train_data_dir} data/lang_chain $ali_dir $tree_dir
  fi
  
  if [ $stage -le 17 ]; then
    mkdir -p $dir
  
    echo "$0: creating neural net configs using the xconfig parser";
  
    num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
    learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
    affine_opts="l2-regularize=0.008 dropout-proportion=0.0 dropout-per-dim-continuous=true"
    tdnnf_opts="l2-regularize=0.008 dropout-proportion=0.0 bypass-scale=0.66"
    linear_opts="l2-regularize=0.008 orthonormal-constraint=-1.0"
    prefinal_opts="l2-regularize=0.008"
    output_opts="l2-regularize=0.002"
  
    mkdir -p $dir/configs
    cat <<EOF > $dir/configs/network.xconfig
    input dim=100 name=ivector
    input dim=40 name=input
  
    # please note that it is important to have input layer with the name=input
    # as the layer immediately preceding the fixed-affine-layer to enable
    # the use of short notation for the descriptor
    fixed-affine-layer name=lda input=Append(-1,0,1,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
  
    # the first splicing is moved before the lda layer, so no splicing here
    relu-batchnorm-dropout-layer name=tdnn1 $affine_opts dim=1024
    tdnnf-layer name=tdnnf2 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=1
    tdnnf-layer name=tdnnf3 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=1
    tdnnf-layer name=tdnnf4 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=1
    tdnnf-layer name=tdnnf5 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=0
    tdnnf-layer name=tdnnf6 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf7 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf8 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf9 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf10 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf11 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf12 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    tdnnf-layer name=tdnnf13 $tdnnf_opts dim=1024 bottleneck-dim=128 time-stride=3
    linear-component name=prefinal-l dim=256 $linear_opts
  
    prefinal-layer name=prefinal-chain input=prefinal-l $prefinal_opts big-dim=1024 small-dim=256
    output-layer name=output include-log-softmax=false dim=$num_targets $output_opts
  
    prefinal-layer name=prefinal-xent input=prefinal-l $prefinal_opts big-dim=1024 small-dim=256
    output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor $output_opts
  EOF
    steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
  
  fi
  
  if [ $stage -le 18 ]; then
    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
      utils/create_split_dir.pl \
       /export/b0{5,6,7,8}/$USER/kaldi-data/egs/ami-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
    fi
  
   steps/nnet3/chain/train.py --stage $train_stage \
      --cmd "$decode_cmd" \
      --feat.online-ivector-dir $train_ivector_dir \
      --feat.cmvn-opts "--norm-means=false --norm-vars=false" \
      --chain.xent-regularize $xent_regularize \
      --chain.leaky-hmm-coefficient 0.1 \
      --chain.l2-regularize 0.0 \
      --chain.apply-deriv-weights false \
      --chain.lm-opts="--num-extra-lm-states=2000" \
      --trainer.dropout-schedule $dropout_schedule \
      --trainer.add-option="--optimization.memory-compression-level=2" \
      --egs.dir "$common_egs_dir" \
      --egs.opts "--frames-overlap-per-eg 0 --constrained false" \
      --egs.chunk-width 150,110,100 \
      --trainer.num-chunk-per-minibatch 64 \
      --trainer.frames-per-iter 5000000 \
      --trainer.num-epochs 6 \
      --trainer.optimization.num-jobs-initial 3 \
      --trainer.optimization.num-jobs-final 12 \
      --trainer.optimization.initial-effective-lrate 0.00025 \
      --trainer.optimization.final-effective-lrate 0.000025 \
      --trainer.max-param-change 2.0 \
      --cleanup.remove-egs $remove_egs \
      --feat-dir $train_data_dir \
      --tree-dir $tree_dir \
      --lat-dir $lat_dir \
      --dir $dir
  fi
  
  
  
  if [ $stage -le 19 ]; then
    # Note: it might appear that this data/lang_chain directory is mismatched, and it is as
    # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
    # the lang directory.
    utils/mkgraph.sh --self-loop-scale 1.0 data/lang $dir $dir/graph
  fi
  
  if [ $stage -le 20 ]; then
    rm $dir/.error 2>/dev/null || true
    for dset in dev test; do
        (
        steps/nnet3/decode.sh --num-threads 4 --nj $decode_nj --cmd "$decode_cmd" \
            --acwt 1.0 --post-decode-acwt 10.0 \
            --online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${dset}_hires \
            --scoring-opts "--min-lmwt 5 " \
           $dir/graph data/${dset}_hires $dir/decode_${dset} || exit 1;
        steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" data/lang data/lang_rescore \
          data/${dset}_hires ${dir}/decode_${dset} ${dir}/decode_${dset}_rescore || exit 1
      ) || touch $dir/.error &
    done
    wait
    if [ -f $dir/.error ]; then
      echo "$0: something went wrong in decoding"
      exit 1
    fi
  fi
  exit 0